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Record W4396896573 · doi:10.1080/13642987.2024.2354167

Losing sight of the abuse: how and why women’s and children’s rights are violated in child contact decisions after intimate partner violence in Europe

2024· article· en· W4396896573 on OpenAlexafffund
Johanna Nelles

Bibliographic record

VenueThe International Journal of Human Rights · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsMcGill University
FundersMcGill University
KeywordsDomestic violenceSightPsychologyChild abuseCriminologySocial psychologyDevelopmental psychologyHuman factors and ergonomicsPoison controlMedical emergencyMedicinePhysics

Abstract

fetched live from OpenAlex

International human rights law sets out the right to life and freedom from torture and ill-treatment. This includes the positive obligation of states to prevent, protect and punish acts of private individuals that threaten the life and limb of another person. Permeating through many areas of law, this duty of due diligence is particularly important for women and children who require protection from abuse perpetrated by intimate partners or family members. The Council of Europe Convention on Preventing and Combating Violence against Women and Domestic Violence details these obligations, including in family law. Based on the work of the convention’s independent monitoring body (GREVIO), this article offers a categorisation of factors and mechanisms that thwart women’s and children’s rights in family law proceedings, resulting in unsafe child contact regulation after intimate partner violence. It shows the shared nature of these factors across several European jurisdictions and places them in context with the interpretative work of international human rights bodies and the European Court of Human Rights. Situating the discussion in the context of growing anti-feminist movements seeking to expand patriarchal notions of family authority, it argues for an understanding of family law processes as a setting for human rights violations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.289
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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